Task_SLO_Processing_Leaf's_Dataset_Laily(2101201026)_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Task SLO Processing Leaf's Dataset- Laily(2101201026) .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/xkonbanx/Task/blob/main/Task_SLO_Processing_Leaf's_Dataset_Laily(2101201026)_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YubG4-HxgthM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22901813-d1fd-4c7f-fa73-cdefe8f61fa8"
      },
      "source": [
        "print('Pembelajaran Secara Statistik dan Optimasi')\n",
        "print('Tugas Dataset Daun')\n",
        "print('Laily')\n",
        "print('2101201026')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pembelajaran Secara Statistik dan Optimasi\n",
            "Tugas Dataset Daun\n",
            "Laily\n",
            "2101201026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzrtFHnWgwEO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afd4d412-8566-4d79-e68d-c092ed7a00e2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M87cPbT1gzES",
        "outputId": "77b33443-f7c6-4586-de46-818606c58315"
      },
      "source": [
        "cd /content/drive/MyDrive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02gZfaQDhUsS",
        "outputId": "ef6a1c8d-5bc2-47e9-b3f4-4dd348883813"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 3scenes-20201129T050009Z-001.zip   \u001b[0m\u001b[01;34mdatadaun\u001b[0m/              forest_test.jpeg\n",
            " best_weight_scene_mobileNet.h5     \u001b[01;34mdataset\u001b[0m/               \u001b[01;34mnnmodel_daun\u001b[0m/\n",
            "\u001b[01;34m'Colab Notebooks'\u001b[0m/                  florest_drfolder.jpg  \u001b[01;34m'semua file'\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbqBRSxlhXAc"
      },
      "source": [
        "# import the necessary packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from PIL import Image\n",
        "from imutils import paths\n",
        "import numpy as np\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mE6dBzG1hZQn",
        "outputId": "3b2889be-1a2d-4c70-d8ef-aa90f1f3c647"
      },
      "source": [
        "# grab all image paths in the input dataset directory, then initialize\n",
        "# our list of images and corresponding class labels\n",
        "print(\"[INFO] loading images...\")\n",
        "imagePaths = paths.list_images(\"datadaun\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading images...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ob48lRhGhbMg"
      },
      "source": [
        "# loop over our input images\n",
        "for imagePath in imagePaths:\n",
        "\t# load the input image from disk, resize it to 32x32 pixels, scale\n",
        "\t# the pixel intensities to the range [0, 1], and then update our\n",
        "\t# images list\n",
        "\timage = Image.open(imagePath)\n",
        "\timage = np.array(image.resize((224, 224))) / 255.0 #normalisasi\n",
        "\tdata.append(image)\n",
        "\n",
        "\t# extract the class label from the file path and update the\n",
        "\t# labels list\n",
        "\tlabel = imagePath.split(os.path.sep)[-2]\n",
        "\tlabels.append(label)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "149vtkIRhdDe",
        "outputId": "1316aa79-0224-4de4-9398-aa8e615e2346"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_05', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01', 'GMB_01']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WB8u7EzhhHI"
      },
      "source": [
        "# encode the labels, converting them from strings to integers\n",
        "lb = LabelBinarizer()\n",
        "labels = lb.fit_transform(labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4tfolnshjAa",
        "outputId": "9f0cb742-f836-4dd7-ee4c-cd37638dda23"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 0 1]\n",
            " [0 0 0 0 1]\n",
            " [0 0 0 0 1]\n",
            " ...\n",
            " [1 0 0 0 0]\n",
            " [1 0 0 0 0]\n",
            " [1 0 0 0 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jXvI7Pqhk3B",
        "outputId": "0f30c129-161f-4610-bb14-027d64653014"
      },
      "source": [
        "# perform a training and testing split, using 75% of the data for\n",
        "# training and 25% for evaluation\n",
        "(trainX, testX, trainY, testY) = train_test_split(np.array(data),\tnp.array(labels), test_size=0.25, shuffle=True)\n",
        "print(trainX.shape)\n",
        "print(testX.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(438, 224, 224, 3)\n",
            "(147, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaKLU7dLhnH1"
      },
      "source": [
        "from keras.applications.mobilenet import MobileNet\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.models import Model\n",
        "from keras.layers import GlobalAveragePooling2D, BatchNormalization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PW9VreWThpUY",
        "outputId": "1e87605c-6ef5-4b68-91f7-5d4ceafb0a0d"
      },
      "source": [
        "from keras.layers import Dropout\n",
        "base_model = MobileNet(weights='imagenet', include_top=False)\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Dense(2048, activation='relu')(x)\n",
        "out = Dense(5, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=base_model.input, outputs=out)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "Model: \"functional_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, None, None, 3)]   0         \n",
            "_________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)    (None, None, None, 3)     0         \n",
            "_________________________________________________________________\n",
            "conv1 (Conv2D)               (None, None, None, 32)    864       \n",
            "_________________________________________________________________\n",
            "conv1_bn (BatchNormalization (None, None, None, 32)    128       \n",
            "_________________________________________________________________\n",
            "conv1_relu (ReLU)            (None, None, None, 32)    0         \n",
            "_________________________________________________________________\n",
            "conv_dw_1 (DepthwiseConv2D)  (None, None, None, 32)    288       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_bn (BatchNormaliza (None, None, None, 32)    128       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_relu (ReLU)        (None, None, None, 32)    0         \n",
            "_________________________________________________________________\n",
            "conv_pw_1 (Conv2D)           (None, None, None, 64)    2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_1_bn (BatchNormaliza (None, None, None, 64)    256       \n",
            "_________________________________________________________________\n",
            "conv_pw_1_relu (ReLU)        (None, None, None, 64)    0         \n",
            "_________________________________________________________________\n",
            "conv_pad_2 (ZeroPadding2D)   (None, None, None, 64)    0         \n",
            "_________________________________________________________________\n",
            "conv_dw_2 (DepthwiseConv2D)  (None, None, None, 64)    576       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_bn (BatchNormaliza (None, None, None, 64)    256       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_relu (ReLU)        (None, None, None, 64)    0         \n",
            "_________________________________________________________________\n",
            "conv_pw_2 (Conv2D)           (None, None, None, 128)   8192      \n",
            "_________________________________________________________________\n",
            "conv_pw_2_bn (BatchNormaliza (None, None, None, 128)   512       \n",
            "_________________________________________________________________\n",
            "conv_pw_2_relu (ReLU)        (None, None, None, 128)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_3 (DepthwiseConv2D)  (None, None, None, 128)   1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_3_bn (BatchNormaliza (None, None, None, 128)   512       \n",
            "_________________________________________________________________\n",
            "conv_dw_3_relu (ReLU)        (None, None, None, 128)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_3 (Conv2D)           (None, None, None, 128)   16384     \n",
            "_________________________________________________________________\n",
            "conv_pw_3_bn (BatchNormaliza (None, None, None, 128)   512       \n",
            "_________________________________________________________________\n",
            "conv_pw_3_relu (ReLU)        (None, None, None, 128)   0         \n",
            "_________________________________________________________________\n",
            "conv_pad_4 (ZeroPadding2D)   (None, None, None, 128)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_4 (DepthwiseConv2D)  (None, None, None, 128)   1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_4_bn (BatchNormaliza (None, None, None, 128)   512       \n",
            "_________________________________________________________________\n",
            "conv_dw_4_relu (ReLU)        (None, None, None, 128)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_4 (Conv2D)           (None, None, None, 256)   32768     \n",
            "_________________________________________________________________\n",
            "conv_pw_4_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_4_relu (ReLU)        (None, None, None, 256)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_5 (DepthwiseConv2D)  (None, None, None, 256)   2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_relu (ReLU)        (None, None, None, 256)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_5 (Conv2D)           (None, None, None, 256)   65536     \n",
            "_________________________________________________________________\n",
            "conv_pw_5_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_5_relu (ReLU)        (None, None, None, 256)   0         \n",
            "_________________________________________________________________\n",
            "conv_pad_6 (ZeroPadding2D)   (None, None, None, 256)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_6 (DepthwiseConv2D)  (None, None, None, 256)   2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_relu (ReLU)        (None, None, None, 256)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_6 (Conv2D)           (None, None, None, 512)   131072    \n",
            "_________________________________________________________________\n",
            "conv_pw_6_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_6_relu (ReLU)        (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_7 (DepthwiseConv2D)  (None, None, None, 512)   4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_relu (ReLU)        (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_7 (Conv2D)           (None, None, None, 512)   262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_7_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_7_relu (ReLU)        (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_8 (DepthwiseConv2D)  (None, None, None, 512)   4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_relu (ReLU)        (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_8 (Conv2D)           (None, None, None, 512)   262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_8_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_8_relu (ReLU)        (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_9 (DepthwiseConv2D)  (None, None, None, 512)   4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_relu (ReLU)        (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_9 (Conv2D)           (None, None, None, 512)   262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_9_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_9_relu (ReLU)        (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_10 (DepthwiseConv2D) (None, None, None, 512)   4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_relu (ReLU)       (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_10 (Conv2D)          (None, None, None, 512)   262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_10_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_10_relu (ReLU)       (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_11 (DepthwiseConv2D) (None, None, None, 512)   4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_relu (ReLU)       (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_11 (Conv2D)          (None, None, None, 512)   262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_11_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_11_relu (ReLU)       (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_pad_12 (ZeroPadding2D)  (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_dw_12 (DepthwiseConv2D) (None, None, None, 512)   4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_relu (ReLU)       (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "conv_pw_12 (Conv2D)          (None, None, None, 1024)  524288    \n",
            "_________________________________________________________________\n",
            "conv_pw_12_bn (BatchNormaliz (None, None, None, 1024)  4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_12_relu (ReLU)       (None, None, None, 1024)  0         \n",
            "_________________________________________________________________\n",
            "conv_dw_13 (DepthwiseConv2D) (None, None, None, 1024)  9216      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_bn (BatchNormaliz (None, None, None, 1024)  4096      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_relu (ReLU)       (None, None, None, 1024)  0         \n",
            "_________________________________________________________________\n",
            "conv_pw_13 (Conv2D)          (None, None, None, 1024)  1048576   \n",
            "_________________________________________________________________\n",
            "conv_pw_13_bn (BatchNormaliz (None, None, None, 1024)  4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_13_relu (ReLU)       (None, None, None, 1024)  0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_2 ( (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 1024)              4096      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 2048)              2099200   \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 5)                 10245     \n",
            "=================================================================\n",
            "Total params: 5,342,405\n",
            "Trainable params: 5,318,469\n",
            "Non-trainable params: 23,936\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLckhClwhrdt"
      },
      "source": [
        "es = EarlyStopping(monitor='val_loss',\n",
        "                   patience=5,\n",
        "                   mode='auto',\n",
        "                   restore_best_weights=True)\n",
        "checkpoint = ModelCheckpoint('best_weight_scene_mobileNet.h5',\n",
        "                             monitor='val_accuracy',\n",
        "                             verbose=1,\n",
        "                             save_best_only=True,\n",
        "                             mode='auto',)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_B2Zm5XhuM_",
        "outputId": "34aaf252-211c-4090-d5c9-d5c7179d8519"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = Adam(lr=1e-3, decay=1e-3 / 50)\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=100, batch_size=32, callbacks=[es, checkpoint])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] training network...\n",
            "Epoch 1/100\n",
            "14/14 [==============================] - ETA: 0s - loss: 0.7419 - accuracy: 0.7671\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.23810, saving model to best_weight_scene_mobileNet.h5\n",
            "14/14 [==============================] - 81s 6s/step - loss: 0.7419 - accuracy: 0.7671 - val_loss: 9.0329 - val_accuracy: 0.2381\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - ETA: 0s - loss: 0.4957 - accuracy: 0.9018\n",
            "Epoch 00002: val_accuracy improved from 0.23810 to 0.24490, saving model to best_weight_scene_mobileNet.h5\n",
            "14/14 [==============================] - 81s 6s/step - loss: 0.4957 - accuracy: 0.9018 - val_loss: 9.7489 - val_accuracy: 0.2449\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - ETA: 0s - loss: 0.5637 - accuracy: 0.9201\n",
            "Epoch 00003: val_accuracy improved from 0.24490 to 0.38095, saving model to best_weight_scene_mobileNet.h5\n",
            "14/14 [==============================] - 83s 6s/step - loss: 0.5637 - accuracy: 0.9201 - val_loss: 9.1626 - val_accuracy: 0.3810\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - ETA: 0s - loss: 0.5523 - accuracy: 0.9155\n",
            "Epoch 00004: val_accuracy did not improve from 0.38095\n",
            "14/14 [==============================] - 80s 6s/step - loss: 0.5523 - accuracy: 0.9155 - val_loss: 12.5531 - val_accuracy: 0.3197\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - ETA: 0s - loss: 0.2527 - accuracy: 0.9543\n",
            "Epoch 00005: val_accuracy did not improve from 0.38095\n",
            "14/14 [==============================] - 80s 6s/step - loss: 0.2527 - accuracy: 0.9543 - val_loss: 13.8566 - val_accuracy: 0.2925\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - ETA: 0s - loss: 0.2673 - accuracy: 0.9498\n",
            "Epoch 00006: val_accuracy did not improve from 0.38095\n",
            "14/14 [==============================] - 80s 6s/step - loss: 0.2673 - accuracy: 0.9498 - val_loss: 11.1002 - val_accuracy: 0.2585\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "l5wSsEwghwMr",
        "outputId": "7e63bded-9386-4366-b2a5-f25c47c855c5"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(H.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(H.history['accuracy'])\n",
        "plt.plot(H.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdSIVOY8hyBu",
        "outputId": "fea9b23a-4317-422f-b541-a5806b6abf21"
      },
      "source": [
        "# evaluate the network\n",
        "print(\"[INFO] evaluating network...\")\n",
        "predictions = model.predict(testX, batch_size=32)\n",
        "print(classification_report(testY.argmax(axis=1),\n",
        "\tpredictions.argmax(axis=1), target_names=lb.classes_))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5Ojqs3qhz4R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58b10fa9-f5ed-4847-af03-de0cf3fd7d43"
      },
      "source": [
        "model.save('nnmodel_daun')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: nnmodel_daun/assets\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}